Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations1597
Missing cells0
Missing cells (%)0.0%
Duplicate rows262
Duplicate rows (%)16.4%
Total size in memory174.7 KiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric12

Alerts

type has constant value "Moscatel"Constant
Dataset has 262 (16.4%) duplicate rowsDuplicates
alcohol is highly overall correlated with chlorides and 1 other fieldsHigh correlation
chlorides is highly overall correlated with alcohol and 1 other fieldsHigh correlation
density is highly overall correlated with alcohol and 3 other fieldsHigh correlation
fixed acidity is highly overall correlated with pHHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
pH is highly overall correlated with fixed acidityHigh correlation
residual sugar is highly overall correlated with densityHigh correlation
total sulfur dioxide is highly overall correlated with density and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-10-09 01:09:54.561835
Analysis finished2024-10-09 01:10:22.133286
Duration27.57 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

type
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
Moscatel
1597 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters12776
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoscatel
2nd rowMoscatel
3rd rowMoscatel
4th rowMoscatel
5th rowMoscatel

Common Values

ValueCountFrequency (%)
Moscatel 1597
100.0%

Length

2024-10-08T22:10:22.280688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T22:10:22.433518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
moscatel 1597
100.0%

Most occurring characters

ValueCountFrequency (%)
M 1597
12.5%
o 1597
12.5%
s 1597
12.5%
c 1597
12.5%
a 1597
12.5%
t 1597
12.5%
e 1597
12.5%
l 1597
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1597
12.5%
o 1597
12.5%
s 1597
12.5%
c 1597
12.5%
a 1597
12.5%
t 1597
12.5%
e 1597
12.5%
l 1597
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1597
12.5%
o 1597
12.5%
s 1597
12.5%
c 1597
12.5%
a 1597
12.5%
t 1597
12.5%
e 1597
12.5%
l 1597
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1597
12.5%
o 1597
12.5%
s 1597
12.5%
c 1597
12.5%
a 1597
12.5%
t 1597
12.5%
e 1597
12.5%
l 1597
12.5%

fixed acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5335629
Minimum3.8
Maximum9.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:22.592836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.4
Q16.1
median6.5
Q36.9
95-th percentile7.7
Maximum9.4
Range5.6
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.70602537
Coefficient of variation (CV)0.10806131
Kurtosis0.83803238
Mean6.5335629
Median Absolute Deviation (MAD)0.4
Skewness0.20130659
Sum10434.1
Variance0.49847182
MonotonicityNot monotonic
2024-10-08T22:10:22.763023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
6.4 119
 
7.5%
6.6 118
 
7.4%
6.8 105
 
6.6%
6 101
 
6.3%
6.7 91
 
5.7%
6.5 84
 
5.3%
6.2 80
 
5.0%
6.3 70
 
4.4%
6.1 70
 
4.4%
6.9 66
 
4.1%
Other values (38) 693
43.4%
ValueCountFrequency (%)
3.8 1
 
0.1%
3.9 1
 
0.1%
4.4 3
 
0.2%
4.7 5
 
0.3%
4.8 7
0.4%
4.9 4
 
0.3%
5 13
0.8%
5.1 10
0.6%
5.2 10
0.6%
5.3 13
0.8%
ValueCountFrequency (%)
9.4 1
 
0.1%
9 2
 
0.1%
8.9 3
0.2%
8.8 3
0.2%
8.7 2
 
0.1%
8.6 4
0.3%
8.5 2
 
0.1%
8.4 2
 
0.1%
8.3 6
0.4%
8.2 3
0.2%

volatile acidity
Real number (ℝ)

Distinct92
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28220726
Minimum0.085
Maximum1.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:22.979982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.085
5-th percentile0.16
Q10.22
median0.27
Q30.33
95-th percentile0.46
Maximum1.1
Range1.015
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.099186523
Coefficient of variation (CV)0.35146694
Kurtosis5.5204166
Mean0.28220726
Median Absolute Deviation (MAD)0.05
Skewness1.5862392
Sum450.685
Variance0.0098379664
MonotonicityNot monotonic
2024-10-08T22:10:23.223471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 97
 
6.1%
0.22 92
 
5.8%
0.24 89
 
5.6%
0.27 75
 
4.7%
0.26 70
 
4.4%
0.3 69
 
4.3%
0.23 66
 
4.1%
0.2 65
 
4.1%
0.32 64
 
4.0%
0.25 57
 
3.6%
Other values (82) 853
53.4%
ValueCountFrequency (%)
0.085 1
 
0.1%
0.09 1
 
0.1%
0.105 4
 
0.3%
0.11 5
 
0.3%
0.12 7
 
0.4%
0.13 8
 
0.5%
0.14 15
 
0.9%
0.145 2
 
0.1%
0.15 31
1.9%
0.16 46
2.9%
ValueCountFrequency (%)
1.1 1
0.1%
0.785 1
0.1%
0.76 1
0.1%
0.75 1
0.1%
0.73 1
0.1%
0.695 2
0.1%
0.69 2
0.1%
0.67 1
0.1%
0.66 1
0.1%
0.655 1
0.1%

citric acid
Real number (ℝ)

Distinct72
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30476518
Minimum0
Maximum1
Zeros8
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:23.462286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.25
median0.29
Q30.34
95-th percentile0.5
Maximum1
Range1
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.10495684
Coefficient of variation (CV)0.34438592
Kurtosis5.2222669
Mean0.30476518
Median Absolute Deviation (MAD)0.04
Skewness1.2629288
Sum486.71
Variance0.011015938
MonotonicityNot monotonic
2024-10-08T22:10:23.676870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 136
 
8.5%
0.3 113
 
7.1%
0.27 103
 
6.4%
0.32 94
 
5.9%
0.26 92
 
5.8%
0.29 90
 
5.6%
0.24 64
 
4.0%
0.25 63
 
3.9%
0.33 62
 
3.9%
0.31 55
 
3.4%
Other values (62) 725
45.4%
ValueCountFrequency (%)
0 8
0.5%
0.01 4
0.3%
0.02 3
 
0.2%
0.04 3
 
0.2%
0.05 1
 
0.1%
0.06 2
 
0.1%
0.09 7
0.4%
0.1 5
0.3%
0.11 1
 
0.1%
0.12 9
0.6%
ValueCountFrequency (%)
1 1
 
0.1%
0.91 2
0.1%
0.86 1
 
0.1%
0.82 1
 
0.1%
0.79 1
 
0.1%
0.78 1
 
0.1%
0.74 1
 
0.1%
0.73 1
 
0.1%
0.72 1
 
0.1%
0.71 3
0.2%

residual sugar
Real number (ℝ)

HIGH CORRELATION 

Distinct221
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4988103
Minimum0.7
Maximum26.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:23.843553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.1
Q11.9
median5.4
Q39.9
95-th percentile15.8
Maximum26.05
Range25.35
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.9611749
Coefficient of variation (CV)0.76339741
Kurtosis-0.28725344
Mean6.4988103
Median Absolute Deviation (MAD)3.7
Skewness0.76019373
Sum10378.6
Variance24.613257
MonotonicityNot monotonic
2024-10-08T22:10:24.067269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 66
 
4.1%
1.1 52
 
3.3%
1.4 51
 
3.2%
1.3 50
 
3.1%
1.6 38
 
2.4%
1.5 35
 
2.2%
2 31
 
1.9%
1 29
 
1.8%
1.8 26
 
1.6%
1.9 22
 
1.4%
Other values (211) 1197
75.0%
ValueCountFrequency (%)
0.7 2
 
0.1%
0.8 5
 
0.3%
0.9 14
 
0.9%
1 29
1.8%
1.1 52
3.3%
1.15 1
 
0.1%
1.2 66
4.1%
1.3 50
3.1%
1.4 51
3.2%
1.45 1
 
0.1%
ValueCountFrequency (%)
26.05 2
0.1%
22.6 1
 
0.1%
20.8 1
 
0.1%
20.3 1
 
0.1%
20.15 1
 
0.1%
19.95 1
 
0.1%
19.9 1
 
0.1%
19.5 1
 
0.1%
19.4 1
 
0.1%
19.3 3
0.2%

chlorides
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045306825
Minimum0.009
Maximum0.271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:24.254789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.027
Q10.035
median0.043
Q30.05
95-th percentile0.064
Maximum0.271
Range0.262
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.020790301
Coefficient of variation (CV)0.45887791
Kurtosis29.87535
Mean0.045306825
Median Absolute Deviation (MAD)0.007
Skewness4.5499503
Sum72.355
Variance0.00043223663
MonotonicityNot monotonic
2024-10-08T22:10:24.474565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036 76
 
4.8%
0.044 69
 
4.3%
0.048 68
 
4.3%
0.042 58
 
3.6%
0.047 58
 
3.6%
0.05 57
 
3.6%
0.035 55
 
3.4%
0.04 54
 
3.4%
0.041 52
 
3.3%
0.037 51
 
3.2%
Other values (89) 999
62.6%
ValueCountFrequency (%)
0.009 1
 
0.1%
0.013 1
 
0.1%
0.014 2
 
0.1%
0.015 4
0.3%
0.016 1
 
0.1%
0.017 3
0.2%
0.018 4
0.3%
0.019 1
 
0.1%
0.02 6
0.4%
0.021 5
0.3%
ValueCountFrequency (%)
0.271 1
0.1%
0.212 1
0.1%
0.209 1
0.1%
0.208 1
0.1%
0.194 1
0.1%
0.185 1
0.1%
0.184 2
0.1%
0.176 2
0.1%
0.175 2
0.1%
0.174 2
0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.991234
Minimum2
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:24.685301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q123
median33
Q345
95-th percentile63
Maximum124
Range122
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.441586
Coefficient of variation (CV)0.46987728
Kurtosis1.867304
Mean34.991234
Median Absolute Deviation (MAD)11
Skewness0.926127
Sum55881
Variance270.32574
MonotonicityNot monotonic
2024-10-08T22:10:24.875890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 56
 
3.5%
26 49
 
3.1%
31 47
 
2.9%
25 45
 
2.8%
36 44
 
2.8%
45 42
 
2.6%
24 41
 
2.6%
34 41
 
2.6%
33 40
 
2.5%
20 40
 
2.5%
Other values (84) 1152
72.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 2
 
0.1%
4 2
 
0.1%
5 6
 
0.4%
6 12
0.8%
7 8
0.5%
8 7
0.4%
9 5
 
0.3%
10 15
0.9%
11 12
0.8%
ValueCountFrequency (%)
124 1
 
0.1%
112 1
 
0.1%
108 3
0.2%
105 2
0.1%
101 2
0.1%
98 3
0.2%
97 1
 
0.1%
87 2
0.1%
81 3
0.2%
79.5 4
0.3%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.07921
Minimum9
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:25.076397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile75
Q1103
median124
Q3154
95-th percentile194
Maximum259
Range250
Interquartile range (IQR)51

Descriptive statistics

Standard deviation37.286478
Coefficient of variation (CV)0.28886509
Kurtosis-0.055848198
Mean129.07921
Median Absolute Deviation (MAD)25
Skewness0.35994336
Sum206139.5
Variance1390.2815
MonotonicityNot monotonic
2024-10-08T22:10:25.269072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 38
 
2.4%
113 34
 
2.1%
122 31
 
1.9%
120 25
 
1.6%
149 24
 
1.5%
140 24
 
1.5%
134 24
 
1.5%
118 23
 
1.4%
116 22
 
1.4%
95 22
 
1.4%
Other values (172) 1330
83.3%
ValueCountFrequency (%)
9 1
 
0.1%
10 1
 
0.1%
31 1
 
0.1%
34 1
 
0.1%
40 3
0.2%
41 2
0.1%
44 1
 
0.1%
47 2
0.1%
49 3
0.2%
50 3
0.2%
ValueCountFrequency (%)
259 1
0.1%
251 1
0.1%
248 2
0.1%
243 1
0.1%
240 1
0.1%
237 2
0.1%
230 1
0.1%
227 1
0.1%
226 1
0.1%
224 2
0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct654
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6138356
Minimum0.98711
Maximum100.295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:25.497936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.989216
Q10.99092
median0.99278
Q30.99544
95-th percentile0.99855
Maximum100.295
Range99.30789
Interquartile range (IQR)0.00452

Descriptive statistics

Standard deviation7.8206588
Coefficient of variation (CV)4.846007
Kurtosis155.19622
Mean1.6138356
Median Absolute Deviation (MAD)0.0022
Skewness12.53003
Sum2577.2955
Variance61.162704
MonotonicityNot monotonic
2024-10-08T22:10:25.747124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9984 15
 
0.9%
0.99792 9
 
0.6%
0.99226 9
 
0.6%
0.99138 9
 
0.6%
0.99058 9
 
0.6%
0.99807 8
 
0.5%
0.99176 8
 
0.5%
0.99184 8
 
0.5%
0.99666 8
 
0.5%
0.99825 7
 
0.4%
Other values (644) 1507
94.4%
ValueCountFrequency (%)
0.98711 1
0.1%
0.98722 1
0.1%
0.9874 1
0.1%
0.98742 2
0.1%
0.98746 2
0.1%
0.98758 1
0.1%
0.98774 1
0.1%
0.98779 1
0.1%
0.98794 2
0.1%
0.98816 1
0.1%
ValueCountFrequency (%)
100.295 2
0.1%
100.196 1
0.1%
100.044 2
0.1%
100.038 2
0.1%
100.037 2
0.1%
100.022 1
0.1%
0.99971 2
0.1%
0.99966 1
0.1%
0.99956 2
0.1%
0.99954 2
0.1%

pH
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.161459
Minimum2.79
Maximum3.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:25.929864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.79
5-th percentile2.938
Q13.07
median3.16
Q33.24
95-th percentile3.39
Maximum3.76
Range0.97
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.13965474
Coefficient of variation (CV)0.044174143
Kurtosis0.63765273
Mean3.161459
Median Absolute Deviation (MAD)0.09
Skewness0.3471881
Sum5048.85
Variance0.019503446
MonotonicityNot monotonic
2024-10-08T22:10:26.161200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 64
 
4.0%
3.14 57
 
3.6%
3.1 55
 
3.4%
3.24 54
 
3.4%
3.18 52
 
3.3%
3.22 51
 
3.2%
3.2 49
 
3.1%
3.12 48
 
3.0%
3.08 48
 
3.0%
3.11 46
 
2.9%
Other values (73) 1073
67.2%
ValueCountFrequency (%)
2.79 1
 
0.1%
2.8 1
 
0.1%
2.82 1
 
0.1%
2.83 4
 
0.3%
2.85 3
 
0.2%
2.86 7
 
0.4%
2.87 3
 
0.2%
2.88 9
 
0.6%
2.89 1
 
0.1%
2.9 23
1.4%
ValueCountFrequency (%)
3.76 1
 
0.1%
3.75 2
0.1%
3.67 1
 
0.1%
3.66 3
0.2%
3.63 1
 
0.1%
3.59 1
 
0.1%
3.57 1
 
0.1%
3.56 2
0.1%
3.55 2
0.1%
3.54 1
 
0.1%

sulphates
Real number (ℝ)

Distinct67
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49013776
Minimum0.23
Maximum1.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:26.379741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.34
Q10.41
median0.48
Q30.55
95-th percentile0.7
Maximum1.08
Range0.85
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.11270309
Coefficient of variation (CV)0.22994166
Kurtosis2.1463277
Mean0.49013776
Median Absolute Deviation (MAD)0.07
Skewness1.0170781
Sum782.75
Variance0.012701986
MonotonicityNot monotonic
2024-10-08T22:10:26.579175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 93
 
5.8%
0.46 65
 
4.1%
0.48 65
 
4.1%
0.44 64
 
4.0%
0.38 62
 
3.9%
0.45 61
 
3.8%
0.54 58
 
3.6%
0.56 55
 
3.4%
0.49 55
 
3.4%
0.52 55
 
3.4%
Other values (57) 964
60.4%
ValueCountFrequency (%)
0.23 1
 
0.1%
0.25 1
 
0.1%
0.26 3
 
0.2%
0.27 6
 
0.4%
0.28 2
 
0.1%
0.29 5
 
0.3%
0.3 9
0.6%
0.31 15
0.9%
0.32 11
0.7%
0.33 16
1.0%
ValueCountFrequency (%)
1.08 1
 
0.1%
1.01 1
 
0.1%
0.98 5
0.3%
0.96 2
 
0.1%
0.94 1
 
0.1%
0.92 1
 
0.1%
0.88 2
 
0.1%
0.85 1
 
0.1%
0.83 2
 
0.1%
0.82 1
 
0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.838798
Minimum8.4
Maximum14.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:26.772949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9
Q19.6
median10.8
Q311.85
95-th percentile13
Maximum14.2
Range5.8
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.3126166
Coefficient of variation (CV)0.12110352
Kurtosis-0.95074176
Mean10.838798
Median Absolute Deviation (MAD)1.1
Skewness0.22488691
Sum17309.56
Variance1.7229622
MonotonicityNot monotonic
2024-10-08T22:10:26.963807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 67
 
4.2%
9.5 61
 
3.8%
11 61
 
3.8%
9.2 59
 
3.7%
11.2 54
 
3.4%
9.1 48
 
3.0%
10.4 46
 
2.9%
10.8 44
 
2.8%
11.1 43
 
2.7%
11.3 42
 
2.6%
Other values (70) 1072
67.1%
ValueCountFrequency (%)
8.4 3
 
0.2%
8.5 4
 
0.3%
8.6 2
 
0.1%
8.7 17
 
1.1%
8.8 31
1.9%
8.9 16
 
1.0%
9 38
2.4%
9.1 48
3.0%
9.2 59
3.7%
9.3 26
1.6%
ValueCountFrequency (%)
14.2 1
 
0.1%
14.05 1
 
0.1%
14 2
 
0.1%
13.9 2
 
0.1%
13.8 2
 
0.1%
13.7 3
 
0.2%
13.6 9
0.6%
13.55 1
 
0.1%
13.5 5
 
0.3%
13.4 15
0.9%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9329994
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-10-08T22:10:27.147176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82330974
Coefficient of variation (CV)0.13876788
Kurtosis0.2411297
Mean5.9329994
Median Absolute Deviation (MAD)0
Skewness0.11795676
Sum9475
Variance0.67783893
MonotonicityNot monotonic
2024-10-08T22:10:27.326940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 799
50.0%
5 413
25.9%
7 293
 
18.3%
8 50
 
3.1%
4 39
 
2.4%
3 3
 
0.2%
ValueCountFrequency (%)
3 3
 
0.2%
4 39
 
2.4%
5 413
25.9%
6 799
50.0%
7 293
 
18.3%
8 50
 
3.1%
ValueCountFrequency (%)
8 50
 
3.1%
7 293
 
18.3%
6 799
50.0%
5 413
25.9%
4 39
 
2.4%
3 3
 
0.2%

Interactions

2024-10-08T22:10:18.867682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:54.870192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:56.474250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.186740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:59.683471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:01.291108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:02.943586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:04.726526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:06.534173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:08.540707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:10.864480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:13.253239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:19.049175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:54.971308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:56.612950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.295316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:59.780349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:01.406777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:03.045691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:04.867934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:06.629928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:08.674481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:11.044923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:13.713629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:19.238788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:55.067692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:56.720921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.385978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:59.925336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:01.514809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:03.152687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:04.989827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:06.733038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:08.827928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:11.204337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:14.099173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:19.418680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:55.171894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:56.839843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.482412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:00.024543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:01.632105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:03.265814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:05.131565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:06.829820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:08.971068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:11.363738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:14.749239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:19.581970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:55.273168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:56.955328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.579069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:00.125871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:01.738386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:03.379803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:05.248673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:06.927994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:09.130762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:11.550107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:15.099715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:19.747184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:55.370713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:57.078663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.679041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:00.244056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:01.838260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:03.484616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:05.362988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:07.027864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:09.277477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:11.704278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:15.538005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:19.955806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:55.496505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:57.179115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.781818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:00.354736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:01.948122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:03.595773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:05.697808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:07.133893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:09.483974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:11.848449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:15.899884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:20.152954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:55.626842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:57.279235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.881391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:00.461277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:02.052570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:03.705050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:05.801251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:07.297558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:09.691252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:12.006003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:16.302907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:20.325737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:55.743307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:57.376492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.985311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:00.573781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:02.156080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:03.810911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:05.900899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:07.497364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:09.872705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:12.164863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:16.821547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:20.469312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:55.873974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:57.478328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:59.103321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:00.698963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:02.327495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:03.928146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:06.003670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:07.669922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:10.065945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:12.360792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:17.298266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:20.612466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:55.982455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:57.566633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:59.214747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:00.822786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:02.444607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:04.039815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:06.101421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:07.889700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:10.234386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:12.518312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:17.672588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:21.445768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:56.350873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:58.086232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:59.578961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:01.186905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:02.837402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:04.589862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:06.431942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:08.382693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:10.732872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:13.077206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:10:18.465411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-08T22:10:27.463059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidevolatile acidity
alcohol1.000-0.6010.010-0.850-0.163-0.2480.2470.446-0.476-0.070-0.4490.146
chlorides-0.6011.0000.0240.5120.1380.145-0.120-0.3450.2320.0830.365-0.037
citric acid0.0100.0241.000-0.0020.2070.081-0.0490.024-0.0640.1770.073-0.160
density-0.8500.512-0.0021.0000.2560.279-0.209-0.3550.8060.1080.514-0.064
fixed acidity-0.1630.1380.2070.2561.000-0.006-0.538-0.1030.1480.0340.092-0.037
free sulfur dioxide-0.2480.1450.0810.279-0.0061.000-0.0170.0030.2550.0780.584-0.079
pH0.247-0.120-0.049-0.209-0.538-0.0171.0000.058-0.2170.082-0.0000.088
quality0.446-0.3450.024-0.355-0.1030.0030.0581.000-0.062-0.021-0.215-0.203
residual sugar-0.4760.232-0.0640.8060.1480.255-0.217-0.0621.0000.0240.3870.040
sulphates-0.0700.0830.1770.1080.0340.0780.082-0.0210.0241.0000.151-0.010
total sulfur dioxide-0.4490.3650.0730.5140.0920.584-0.000-0.2150.3870.1511.0000.102
volatile acidity0.146-0.037-0.160-0.064-0.037-0.0790.088-0.2030.040-0.0100.1021.000

Missing values

2024-10-08T22:10:21.645894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-08T22:10:21.960952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0Moscatel8.10.240.3210.50.03034.0105.00.994073.110.4211.86
1Moscatel5.80.230.202.00.04339.0154.00.992263.210.3910.26
2Moscatel7.50.330.362.60.05126.0126.00.990973.320.5312.76
3Moscatel6.60.380.369.20.06142.0214.00.997603.310.569.45
4Moscatel6.40.150.291.80.04421.0115.00.991663.100.3810.25
5Moscatel6.50.320.345.70.04427.091.00.991843.280.60127
6Moscatel7.50.220.322.40.04529.0100.00.991353.080.6011.37
7Moscatel6.40.230.321.90.03840.0118.00.990743.320.5311.87
8Moscatel6.10.220.311.40.03940.0129.00.991933.450.5910.95
9Moscatel6.50.480.020.90.04332.099.00.992263.140.479.84
typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
1622Moscatel6.80.2200.361.200.05238.0127.00.993303.040.549.25
1623Moscatel4.90.2350.2711.750.03034.0118.00.995403.070.509.46
1624Moscatel6.10.3400.292.200.03625.0100.00.989383.060.4411.86
1625Moscatel5.70.2100.320.900.03838.0121.00.990743.240.4610.66
1626Moscatel6.50.2300.381.300.03229.0112.00.992983.290.549.75
1627Moscatel6.20.2100.291.600.03924.092.00.991143.270.5011.26
1628Moscatel6.60.3200.368.000.04757.0168.00.994903.150.469.65
1629Moscatel6.50.2400.191.200.04130.0111.00.992542.990.469.46
1630Moscatel5.50.2900.301.100.02220.0110.00.988693.340.3812.87
1631Moscatel6.00.2100.380.800.02022.098.00.989413.260.3211.86

Duplicate rows

Most frequently occurring

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
192Moscatel7.00.150.2814.700.05129.0149.00.997922.960.39978
227Moscatel7.30.190.2713.900.05745.0155.00.998072.940.418.888
233Moscatel7.40.160.3013.700.05633.0168.00.998252.900.448.777
232Moscatel7.40.160.2715.500.05025.0135.00.998402.900.438.776
12Moscatel5.70.220.2016.000.04441.0113.00.998623.220.468.965
123Moscatel6.60.220.2317.300.04737.0118.00.999063.080.468.865
140Moscatel6.70.160.3212.500.03518.0156.00.996662.880.36965
239Moscatel7.50.240.3113.100.05026.0180.00.998843.050.539.165
13Moscatel5.70.220.2216.650.04439.0110.00.998553.240.48964
27Moscatel6.00.200.266.800.04922.093.00.992803.150.421164